{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mw2VBrBcgvGa"
   },
   "source": [
    "In this exercise you'll try to build a neural network that predicts the price of a house according to a simple formula.\n",
    "\n",
    "So, imagine if house pricing was as easy as a house costs 50k + 50k per bedroom, so that a 1 bedroom house costs 100k, a 2 bedroom house costs 150k etc.\n",
    "\n",
    "How would you create a neural network that learns this relationship so that it would predict a 7 bedroom house as costing close to 400k etc.\n",
    "\n",
    "Hint: Your network might work better if you scale the house price down. You don't have to give the answer 400...it might be better to create something that predicts the number 4, and then your answer is in the 'hundreds of thousands' etc.\n",
    "\n",
    "在本练习中，您将尝试构建一个神经网络，让它根据一个简单的公式来预测房屋的价格。\n",
    "\n",
    "想象一下，如果房子的定价很简单，带一间卧室的房子价格是5万+5万，那么一间卧室的房子要花10万元；两间卧室的房子就要花15万元，如此类推。\n",
    "\n",
    "如何创建一个神经网络，来学习这种关系，让它会预测一个7间卧室的房子，价格接近40万。\n",
    "\n",
    "提示：如果将房价单位用10万表示（称为scale），网络判断准确性会更好。例如对于x=1，输出1，表示10万；x=2，输出1.5表示15万。神经元网络对大数值处理不是太好，一般训练数据都要经过scalling变小才行。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "PUNO2E6SeURH",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
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      "Epoch 964/1000\n",
      "1/1 [==============================] - 0s 673us/step - loss: 1.3303e-05\n",
      "Epoch 965/1000\n",
      "1/1 [==============================] - 0s 999us/step - loss: 1.3206e-05\n",
      "Epoch 966/1000\n",
      "1/1 [==============================] - 0s 876us/step - loss: 1.3110e-05\n",
      "Epoch 967/1000\n",
      "1/1 [==============================] - 0s 637us/step - loss: 1.3014e-05\n",
      "Epoch 968/1000\n",
      "1/1 [==============================] - 0s 858us/step - loss: 1.2920e-05\n",
      "Epoch 969/1000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 1.2826e-05\n",
      "Epoch 970/1000\n",
      "1/1 [==============================] - 0s 980us/step - loss: 1.2732e-05\n",
      "Epoch 971/1000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 1.2639e-05\n",
      "Epoch 972/1000\n",
      "1/1 [==============================] - 0s 856us/step - loss: 1.2547e-05\n",
      "Epoch 973/1000\n",
      "1/1 [==============================] - 0s 998us/step - loss: 1.2456e-05\n",
      "Epoch 974/1000\n",
      "1/1 [==============================] - 0s 877us/step - loss: 1.2365e-05\n",
      "Epoch 975/1000\n",
      "1/1 [==============================] - 0s 767us/step - loss: 1.2275e-05\n",
      "Epoch 976/1000\n",
      "1/1 [==============================] - ETA: 0s - loss: 1.2186e-0 - 0s 1ms/step - loss: 1.2186e-05\n",
      "Epoch 977/1000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 1.2097e-05\n",
      "Epoch 978/1000\n",
      "1/1 [==============================] - 0s 822us/step - loss: 1.2009e-05\n",
      "Epoch 979/1000\n",
      "1/1 [==============================] - 0s 801us/step - loss: 1.1921e-05\n",
      "Epoch 980/1000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 1.1834e-05\n",
      "Epoch 981/1000\n",
      "1/1 [==============================] - 0s 977us/step - loss: 1.1748e-05\n",
      "Epoch 982/1000\n",
      "1/1 [==============================] - 0s 832us/step - loss: 1.1663e-05\n",
      "Epoch 983/1000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 1.1578e-05\n",
      "Epoch 984/1000\n",
      "1/1 [==============================] - 0s 765us/step - loss: 1.1493e-05\n",
      "Epoch 985/1000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 1.1409e-05\n",
      "Epoch 986/1000\n",
      "1/1 [==============================] - 0s 854us/step - loss: 1.1326e-05\n",
      "Epoch 987/1000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 1.1244e-05\n",
      "Epoch 988/1000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 1.1162e-05\n",
      "Epoch 989/1000\n",
      "1/1 [==============================] - ETA: 0s - loss: 1.1081e-0 - 0s 1ms/step - loss: 1.1081e-05\n",
      "Epoch 990/1000\n",
      "1/1 [==============================] - 0s 766us/step - loss: 1.1000e-05\n",
      "Epoch 991/1000\n",
      "1/1 [==============================] - 0s 968us/step - loss: 1.0920e-05\n",
      "Epoch 992/1000\n",
      "1/1 [==============================] - 0s 836us/step - loss: 1.0840e-05\n",
      "Epoch 993/1000\n",
      "1/1 [==============================] - 0s 955us/step - loss: 1.0761e-05\n",
      "Epoch 994/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.0683e-05\n",
      "Epoch 995/1000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 1.0605e-05\n",
      "Epoch 996/1000\n",
      "1/1 [==============================] - 0s 730us/step - loss: 1.0528e-05\n",
      "Epoch 997/1000\n",
      "1/1 [==============================] - 0s 992us/step - loss: 1.0451e-05\n",
      "Epoch 998/1000\n",
      "1/1 [==============================] - 0s 917us/step - loss: 1.0375e-05\n",
      "Epoch 999/1000\n",
      "1/1 [==============================] - 0s 922us/step - loss: 1.0300e-05\n",
      "Epoch 1000/1000\n",
      "1/1 [==============================] - 0s 831us/step - loss: 1.0224e-05\n",
      "[[4.0046124]]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from tensorflow import keras\n",
    "model = tf.keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])])\n",
    "model.compile(optimizer='sgd', loss='mean_squared_error')\n",
    "xs = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], dtype=float)\n",
    "ys = np.array([1.0, 1.5, 2.0, 2.5, 3.0, 3.5], dtype=float)\n",
    "model.fit(xs, ys, epochs=1000)\n",
    "print(model.predict([7.0]))"
   ]
  }
 ],
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